The present invention relates to an information filtering method, an information filtering device and a storage medium storing an information filtering program, for carrying out recommendation or filtering of information according to a user""s interest or taste from an information system, such as database or the WWW (world wide web).
Information filtering methods and devices, and information filtering programs (hereinafter collectively referred to as xe2x80x9cinformation filtering systemsxe2x80x9d) have been used to carry out recommendation or filtering-in of information or services matching with interests or tastes of users from among a large amount of information or a large number of services (hereinafter collectively referred to as xe2x80x9cinformationxe2x80x9d) offered by an information system, such as database, personal computer communication or the WWW.
In the conventional information filtering systems, there have been known two kinds of systems, that is, the CBF (content based filtering) system and the SIF (social information filtering) system.
In the CBF system, the contents of items of information are featured by data, such as keywords or frequencies of words contained in the respective information items (hereinafter such data will be referred to as xe2x80x9cattributesxe2x80x9d), and the information filtering is performed based on comparison between the foregoing featured contents and interests of a user shown in the past and represented by weights of keywords or the like.
On the other hand, in the SIF system, data about ratings of information items carried out by users (hereinafter such data will be referred to as xe2x80x9cratingsxe2x80x9d) are stored, and the information filtering is performed by comparing the stored ratings of a subject user (a user requesting the information filtering) and the stored ratings of the users other than the subject user to find the user/users having similar taste with the subject user and by selecting the information item which has been evaluated high by the foregoing similar user/users, but not yet evaluated by the subject user.
Japanese First (unexamined) Patent Publication No. 3-94375 describes a document retrieval device as an example of the CBF system. On the other hand, as examples of the SIP system, a system called GroupLens is described in xe2x80x9cproceedings of the cscw 1994xe2x80x9d by acm press, 1994, pp. 175-186 and a system called Ringo is described in xe2x80x9cproceedings of the chi95xe2x80x9d by acm press, 1995, pp. 210-217.
Each of the CBF and the SIF will far later be described in detail in conjunction with the drawing.
In the CBF system, a relationship between attributes of information items, such as word frequencies, and ratings performed by the subject user is learned relative to information items which have been rated by the subject user, so as to find (filter in) information items which have not yet been rated by the subject user. On the other hand, in the foregoing SIF system, a relationship between the subject user and the users other than the subject user is learned from ratings carried out by the subject user and the other users relative to the information items so as to filter the information items which have not been rated by the subject user, but rated by the other users.
Specifically, the first problem of the CBF system resides in that, although information about what the user is interested in (that is, attributes such as keywords) can be obtained, it is necessary for the user to judge whether the information is valuable or not, by reading it. The reason is that the CBF system is a system based on the attributes of keywords or word frequencies and not based on personal evaluations as the SIF system.
The second problem of the CBF system resides in that the filtering accuracy is low relative to an information item having an unknown word or an unknown attribute value. The reason is that, in the CBF system, the relationship between the ratings by the subject user and the attributes of the information items is learned as the profile based on the rating history of the subject user and the attributes of the information items already rated by the subject user, and the relevances are estimated based on the learned profile. Accordingly, in the CBF system, if the information item has an unknown word or an unknown attribute value, how to reflect it upon the relevance is unknown.
On the other hand, the first problem of the SIF system resides in that only the information item that has been recommended or evaluated by the users can be obtained. The reason is that the SIF system is a system based on the recommendation or evaluation of the users so that only such a recommended or evaluated information item can be the subject of the filtering.
The second problem of the SIF system resides in that the filtering accuracy is low unless a certain number of ratings have been collected. The reason is that the SIF system is a system based on the recommendation or evaluation of the users.
It is therefore an object of the present invention to provide an information filtering system which is capable of reducing or eliminating the foregoing problems inherent in the conventional CBF and SIF systems and performing recommendation or filtering-in of information with higher filtering accuracy and with more agreement to a user""s interest or taste.
Other objects of this invention will become clear as the description proceeds.
According to an aspect of the present invention, there is provided an information filtering method comprising the steps of obtaining attributes included in information items, obtaining ratings relative to the information items performed by users including a subject user and other users, estimating relevances to the subject user of the information items not rated by the subject user by the use of the ratings relative to the information items rated by the subject user, of the attributes of the information items rated by the subject user, of the ratings relative to the information items rated by the other users, and of the attributes of the information items rated by the other users, and carrying out recommendation or filtering-in for the subject user as regards the information items by the use of the relevances.
According to another aspect of the present invention, there is provided an information filtering method comprising first obtaining means for obtaining attributes included in information items, second obtaining means for obtaining ratings relative to the information items performed by users including a subject user and other users, estimating means connected to the first and the second obtaining means for estimating relevances to the subject user of the information items not rated by the subject user by the use of the ratings relative to the information items rated by the subject user, of the attributes of the information items rated by the subject user, of the ratings relative to the information items rated by the other users, and of the attributes of the information items rated by the other users, and carrying out means connected to the estimating means for carrying out recommendation or filtering-in for the subject user as regards the information items by the use of the relevances.
According to still another aspect of the present invention, there is provided a storage medium storing an information filtering program which is executable by a computer. The information filtering program allows the computer to execute the steps of extracting attributes included in information items and storing the attributes in a storage device of the computer, and storing ratings relative to the information items performed by users including a subject user and other users in the storage device; using a relationship between the ratings relative to the information items rated by the subject user and the attributes thereof and a relationship between the ratings relative to the information items rated by the other users and the attributes thereof so as to estimate relevances to the subject user of the information items not rated by the subject user; and using the relevances to carry out recommendation or filtering-in of the information item matching with the subject user.
According to yet another aspect of the present invention, there is provided a storage medium storing an information filtering program which is executable by a computer. The information filtering program allows the computer to function as: attribute extracting means for extracting attributes included in information items; attribute storing means for storing the extracted attributes; rating storing means for receiving inputs of rating values relative to the information items from users including a subject user and other users, and storing the received rating values; similarity deriving means for deriving, by referring to the rating storing means, similarities between the subject user and the other users based on the rating values inputted by the subject user relative to the information items and the rating values inputted by the other users relative to the information items; first relevance estimating means for estimating, based on the derived similarities, first relevances to the subject user of the information items not rated by the subject user among the information items rated by the other users; a user profile learning means for learning, by referring to the attribute storing means and the rating storing means, a relationship between the attributes included in the information items rated by the subject user and the ratings thereof, and further learning, based on the estimated first relevances, a relationship between the attributes included in the information items not rated by the subject user among the information items rated by the other users and the relevances thereof to the subject user; second relevance estimating means for estimating, based on the relationship between the attributes and the ratings and the relationship between the attributes and the relevances, second relevances to the subject user of the information items not rated by any of the subject user and the other users; and relevance unifying means for unifying the first and second relevances to carry out recommendation or filtering-in of the information item matching with the subject user.